Evaluation of regional water security using water poverty index.
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Bibliographic record
Abstract
Water security is a widely concerned issue in the world nowadays. A new method, water poverty index (WPI), was applied to evaluate the regional water security. Twelve state farms in Heilongjiang Province, Northeastern China were selected to evaluate water security status based on the data of 2006 using WPI and mean deviation grading method. The method of WPI includes five key indices: resources(R), access (A), capacity(C), utilization (U) and environment (E). Each key index further consists of several sub-indices. According to the results of WPI, the grade of each farm was calculated by using the method of mean deviation grading. Thus, the radar images can be protracted of each farm. From the radar images, the conclusions can be drawn that the WPI values of Farm 853 and Hongqiling are under very safe status, while that of Farm Raohe is under safe status, those of Farms Youyi, 597, 852, 291 and Jiangchuan are under moderate safe status, that of Farm Beixing is under low safe status and those of Farm Shuangyashan, Shuguang and Baoshan are under unsafe status. The results from this study can provide basic information for decision making on rational utilization of water resources and regulations for regional water safety guarantee system. Keywords: mean deviation grading method, water poverty index, water security evaluation, weighted average method DOI: 10.3965/j.issn.1934-6344.2008.02.008-014 Citation: Fu Qiang, Gary Kachanoski, Liu Dong, Wang Zilong. Evaluation of regional water security using water poverty index. Int J Agric & Biol Eng. 2008; 1(2): 8
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it